A Robust Predictive Current Control of Induction Motor Drives

Abstract

Model predictive control (MPC) can achieve excellent control results for induction motors (IMs) by selecting the optimal voltage vector. However, a large number of motor parameters are used in the control algorithm. Therefore, the performance of MPC is highly dependent on the accuracy of the motor parameters, which means that the dynamic-state and steady-state performances will be affected when the motor parameters are not accurately set in the controller. To achieve robustness against mismatches of motor parameters, this paper proposes a new robust predictive current control (RPCC) for IMs. In the proposed method, an ultra-local model is used to replace the mathematical model of the IM, and an linear extended state observer (LESO) is applied to estimate the disturbance to improve the control performance. The entire controller only needs system input and output data without the motor parameters. Moreover, satisfactory control performance can still be obtained when the motor parameters deviate from their nominal values. The experimental results confirm the effectiveness of the proposed method.